ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0

• Main Authors: Nagler, Thomas, Schwaizer, Gabriele, Mölg, Nico, Keuris, Lars, Hetzenecker, Markus, Metsämäki, Sari
• Format: Dataset
• Language: English
• Published: NERC EDS Centre for Environmental Data Analysis 2022
• Subjects: Atmospheric sciences and Earth Observation Imagery; Greenland; Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.5285/8847a05eeda646a29da58b42bdf2a87c; https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c

This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2020. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. Improvements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development. There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.